D214 - Data Analytics Graduate Capstone

Correlation Between the Estimated Sentiments of Tweets and the Change in Bitcoin Prices

---

#importing the needed libraries
import pandas as pd
#modified Pandas options to fully display the large dataset when needed
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

import re
from textblob import TextBlob

import scipy.stats as stats
import sklearn
from sklearn.preprocessing import StandardScaler
from sklearn import linear_model
import statsmodels.api as sm

import warnings
warnings.filterwarnings('ignore') # Ignore warning messages for readability

# Bitcoin twitter Data
#https://www.kaggle.com/kaushiksuresh147/bitcoin-tweets
#Bitcoin Tweets
df_Bitcoin_tweets = pd.read_csv('Bitcoin_tweets.csv')
df_Bitcoin_tweets.drop_duplicates(subset=['user_followers','date','text'],inplace=True)
df_Bitcoin_tweets.sort_values(by='date',inplace=True)
print('df_Bitcoin_tweets dimensions= ',df_Bitcoin_tweets.shape)
df_Bitcoin_tweets=df_Bitcoin_tweets[['user_followers','date','text']]
df_Bitcoin_tweets.head()

df_Bitcoin_tweets dimensions=  (346512, 13)

	user_followers	date	text
21523	301.0	2021-02-05 10:52:04	2⃣ Debunking 9 #Bitcoin Myths by @Patrick_Lowry_ ⬇️ \n\n#cryptocurrency #bitcoin #crypto #blockchain #btc… https://t.co/2CM83fuB2n
21524	301.0	2021-02-05 10:52:04	📖 Weekend Read 📖\n\nKeen to learn about #crypto assets? Check out our reading list! \n1⃣ 2021 CryptoMarket Outlook… https://t.co/opiev94qzl
21522	301.0	2021-02-05 10:52:06	4⃣ 🎙️ Bloomberg LP #CryptoOutlook 2021 with @mikemcglone11 ⬇️\n\n#cryptocurrency #bitcoin #crypto #blockchain #btc… https://t.co/gC3VNGAP6v
21521	301.0	2021-02-05 10:52:07	5⃣ #Blockchain 50 2021 by @DelRayMan, @Forbes , @ForbesCrypto ⬇️\n\n#cryptocurrency #bitcoin #crypto #blockchain… https://t.co/L3Xj7j49Fx
21520	37.0	2021-02-05 10:52:26	#reddcoin #rdd @reddcoin to the moon #altcoin #turnreddcoinin1dollar #eth #btc #bitcoin https://t.co/ycm15VolsW

# Bitcoin Price Data
#BTCUSD
#https://www.kaggle.com/maxwells/btcusd
df_Bitcoin_Price= pd.read_csv('BTCUSD_1.csv')
df_Bitcoin_Price['time'] = pd.to_datetime(df_Bitcoin_Price['time'])
# Set time column as index
df_Bitcoin_Price.set_index('time',drop=True,inplace=True)
print('df_Bitcoin_Price dimensions= ',df_Bitcoin_Price.shape)
df_Bitcoin_Price=df_Bitcoin_Price[['close']]
df_Bitcoin_Price.tail()

df_Bitcoin_Price dimensions=  (1629299, 5)

	close
time
2021-06-15 15:56:00	40251.14
2021-06-15 15:57:00	40308.39
2021-06-15 15:58:00	40345.39
2021-06-15 15:59:00	40406.24
2021-06-15 16:00:00	40378.25

# Plot and show the time series on axis ax
fig, ax = plt.subplots(figsize = (14,8))
plt.plot(df_Bitcoin_Price.index, 
         (df_Bitcoin_Price[['close']]), 
         linewidth=1,)
#plt.ylim([0,3500])
plt.legend(['Price-diff'],loc='upper left')

plt.grid()
plt.show()

### Dataset Cleaning - tweets cleaning
# Edited after (Naik,2020)

def cleantxt(text):
    text=str(text)
    text = text.lower() #Converting Capital chars into small
    text = re.sub(r'@[a-zA-Z0-9]+', ' ', text) #Removing @ mentions
    text = re.sub("#bitcoin", 'bitcoin', text) # removeing the '#' from bitcoin
    text = re.sub("#btc", 'btc', text) # removeing the '#' from btc
    #text = re.sub('#[A-Za-z0-9]+', '', text) # removing any hastag or string starting with a '#'
    text = re.sub('\\n', '', text) # removing the '\n' string
    text = re.sub(r'https?:\/\/\S+', ' ', text) #Removing hyper links
    #text = re.sub(r'[^a-zA-Z]+', ' ', text) #Removing non ASCII chars
    text = text.split() #Removing extra spaces
    text = ' '.join(text)
    return text

### Dataset Cleaning - date cleaning

def cleandate(date):
    date=str(date)
    date = re.sub(r'[^0-9\-\:]', ' ', date) #Removing non ASCII chars
    date = date.split() #Removing extra spaces
    date = ' '.join(date)
    return date

df_Bitcoin_tweets['text']=df_Bitcoin_tweets['text'].apply(cleantxt)
df_Bitcoin_tweets['date']=df_Bitcoin_tweets['date'].apply(cleandate)
df_Bitcoin_tweets.dropna(inplace=True)
df_Bitcoin_tweets.drop(df_Bitcoin_tweets[df_Bitcoin_tweets['text'].str.len()==0].index,inplace=True)

df_Bitcoin_tweets['date'] = pd.to_datetime(df_Bitcoin_tweets['date'])

# Set date column as index
df_Bitcoin_tweets.set_index('date',drop=False, inplace=True)

def getsubjectivity(text):
    return TextBlob(text).sentiment.subjectivity
def getpolarity(text):
    return TextBlob(text).sentiment.polarity

df_Bitcoin_tweets['subj']=df_Bitcoin_tweets['text'].apply(getsubjectivity)
df_Bitcoin_tweets['polarity']=df_Bitcoin_tweets['text'].apply(getpolarity)
df_Bitcoin_tweets['count']=1

df_Bitcoin_tweets['polarity'] = df_Bitcoin_tweets['polarity'].apply(lambda x: 1 if x > 0 else (-1 if x < 0 else x))
df_Bitcoin_tweets.head(10)

	user_followers	date	text	subj	polarity	count
date
2021-02-05 10:52:04	301.0	2021-02-05 10:52:04	2⃣ debunking 9 bitcoin myths by _lowry_ ⬇️ #cryptocurrency bitcoin #crypto #blockchain btc…	0.000000	0.0	1
2021-02-05 10:52:04	301.0	2021-02-05 10:52:04	📖 weekend read 📖keen to learn about #crypto assets? check out our reading list! 1⃣ 2021 cryptomarket outlook…	0.000000	0.0	1
2021-02-05 10:52:06	301.0	2021-02-05 10:52:06	4⃣ 🎙️ bloomberg lp #cryptooutlook 2021 with ⬇️#cryptocurrency bitcoin #crypto #blockchain btc…	0.000000	0.0	1
2021-02-05 10:52:07	301.0	2021-02-05 10:52:07	5⃣ #blockchain 50 2021 by , , ⬇️#cryptocurrency bitcoin #crypto #blockchain…	0.000000	0.0	1
2021-02-05 10:52:26	37.0	2021-02-05 10:52:26	#reddcoin #rdd to the moon #altcoin #turnreddcoinin1dollar #eth btc bitcoin	0.000000	0.0	1
2021-02-05 10:53:49	100.0	2021-02-05 10:53:49	bitcoin and #eth both have bullish setups for a move higher ... btc it would just be great if daily close (in abo…	0.416667	1.0	1
2021-02-05 10:54:52	69.0	2021-02-05 10:54:52	$perl 0.06.i have insisted that since 0.02 it will be 0.071. it increased 300% in about 2 months. bitcoin btc…	0.000000	0.0	1
2021-02-05 10:57:05	163.0	2021-02-05 10:57:05	#amazing 😍#monopoly #crypto #cryptocurrency #cryptocurrencies #cryptonews #cryptotrading #ripple #xrp…	0.900000	1.0	1
2021-02-05 10:58:03	2520.0	2021-02-05 10:58:03	$juld $bnb #binance #bsc #binancesmartchain btc bitcoin $btc $eth $uni $sushi $cake #1inch $link $xrp…	0.000000	0.0	1
2021-02-05 10:58:36	1036.0	2021-02-05 10:58:36	amazoncoin = 100% love💚 👉👉 #amazoncoin #crypto #defi #cryptocurrency…	0.000000	0.0	1

df_Bitcoin_tweets['polarity'].hist(bins=5)

<AxesSubplot:>

df_Bitcoin_tweets=df_Bitcoin_tweets.resample('Min').sum()

# Plot and show the time series on axis ax
fig, ax = plt.subplots(figsize = (14,8))
df_Bitcoin_tweets[['count','subj','polarity']].plot(ax=ax)
plt.grid()
plt.show()

df_Bitcoin_Price['diff']=df_Bitcoin_Price['close'].diff()
df_Bitcoin_Price=df_Bitcoin_Price[['diff']]

start_time=max(df_Bitcoin_tweets.index.min(),df_Bitcoin_Price.index.min())
end_time=min(df_Bitcoin_tweets.index.max(),df_Bitcoin_Price.index.max())
df_Bitcoin_Price=df_Bitcoin_Price.loc[(df_Bitcoin_Price.index >= start_time) & (df_Bitcoin_Price.index  <= end_time)]
df_Bitcoin_tweets=df_Bitcoin_tweets.loc[(df_Bitcoin_tweets.index >= start_time) & (df_Bitcoin_tweets.index  <= end_time)]

df_Bitcoin_price_tweets = pd.concat([df_Bitcoin_Price, df_Bitcoin_tweets], axis=1)

# Plot and show the time series on axis ax
fig, ax = plt.subplots(figsize = (14,8))
plt.plot(df_Bitcoin_price_tweets.index, 
         (df_Bitcoin_price_tweets[['diff']]),
         color='gray', 
         linewidth=1,)
#plt.ylim([0,3500])
plt.legend(['Price-diff'],loc='upper left')

ax.twinx()

plt.plot(df_Bitcoin_price_tweets.index, 
         df_Bitcoin_price_tweets[['count','subj','polarity']],
         linewidth=1,)
#plt.ylim([0,100])
plt.legend(['count','subj','polarity'],loc='upper right')

plt.grid()
plt.show()

df_Bitcoin_price_tweets['count'].replace(0.0, np.NaN,inplace=True) #
df_Bitcoin_price_tweets=df_Bitcoin_price_tweets[(~df_Bitcoin_price_tweets['count'].isnull() & ~df_Bitcoin_price_tweets['diff'].isnull())]

# Plot and show the time series on axis ax
fig, ax = plt.subplots(figsize = (14,8))
plt.plot(df_Bitcoin_price_tweets.index, 
         (df_Bitcoin_price_tweets[['diff']]),
         color='gray', 
         linewidth=1,)
#plt.ylim([0,3500])
plt.legend(['Price-diff'],loc='upper left')

ax.twinx()

plt.plot(df_Bitcoin_price_tweets.index, 
         df_Bitcoin_price_tweets[['count','subj','polarity']],
         linewidth=1,)
#plt.ylim([0,100])
plt.legend(['count','subj','polarity'],loc='upper right')

plt.grid()
plt.show()

df_Bitcoin_price_tweets_1min=df_Bitcoin_price_tweets.copy()
#df_Bitcoin_price_tweets.index=df_Bitcoin_price_tweets.index.astype(str)
df_Bitcoin_price_tweets.reset_index(drop=True,inplace= True)

print('df_Bitcoin_price_tweets dimensions= ',df_Bitcoin_price_tweets.shape) 

# Plot and show the time series on axis ax
fig, ax = plt.subplots(figsize = (14,8))
plt.plot(df_Bitcoin_price_tweets.index, 
         (df_Bitcoin_price_tweets[['diff']]),
         color='gray', 
         linewidth=1,)
#plt.ylim([0,3500])
plt.legend(['Price-diff'],loc='upper left')

ax.twinx()

plt.plot(df_Bitcoin_price_tweets.index, 
         df_Bitcoin_price_tweets[['count','subj','polarity']],
         linewidth=1,)
#plt.ylim([0,100])
plt.legend(['count','subj','polarity'],loc='upper right')

plt.grid()
plt.show()

df_Bitcoin_price_tweets dimensions=  (36771, 5)

'''Custom function to inspect the data visually using Histogram/Bar, Boxplot and scattered plots
this function takes 3 parameters, 1st is DataFrame , 2nd is the preferable plot ("hist" , "box" , " scat" , "target_scat" or "target_histplot" )
The target argument will be used only in the case of target vs variables scat plots, and will not be used for other options
For Histograms with non-numeric columns , the function will use Bar charts (in Green color) instead of Histogram
"scat": plotting scatterd plot against the 1st column of the D.F.
"target_scat": plotting scatterd plot of num. variables against a 'target' column of the D.F.
"target_histplot":plotting histplot of numeric or countplot of cat. variables against the target' column 
'''

def plt_summary(df_1,plt_type,target):
    numerics = ['uint8','uint16','uint32','int16', 'int32', 'int64', 'float16', 'float32', 'float64']
    
    if plt_type =='target_histplot':
        pltlist=df_1.columns
#         for number,cols in enumerate(df_1.columns):
#             if (len(df_1[cols].value_counts())>=20):
#                 pltlist.remove(cols)
        print("Plot Type:  "+plt_type) #Printing plot type.
        print("Number of columns:  "+str(len(pltlist))) #Printing the number of columns.
        
        
    elif plt_type =='hist':
        pltlist=df_1.columns
        print("Plot Type:  "+plt_type) #Printing plot type.
        print("Number of columns:  "+str(df_1.shape[1])) #Printing the number of columns.
        
    else :
        df_n = df_1.select_dtypes(include=numerics).copy()   # selecting only the numeric columns as it's suitable for mentioned plots
        pltlist=df_n.columns
        print("Plot Type:  "+plt_type) #Printing plot type.
        print("Number of Numeric columns:  "+str(df_n.shape[1])) #Printing the number of numeric columns.

    
    plt.style.use('default')
    plt.figure(figsize=(15,30))
    plt_rows = 11
    plt_cols = 5
    
    for n, col in enumerate(pltlist):   #enumerating each column of the data, starting from index
        b=n+1
        ax=plt.subplot2grid(shape=(plt_rows,plt_cols),loc=((int(np.ceil(b/plt_cols)-1)),((b-1)%plt_cols)))
        if plt_type == 'hist':
            if df_1[col].dtype in numerics:
                ax.hist(df_1.loc[:,col],bins = 50)  #plotting Histogram
                ax.set_title(str(col)+' (*num)')
            else:
                if len(df_1[col].value_counts())>2000:
                    ax.set_title(str(col)+' ('+str(len(df_1[col].value_counts()))+' unq val.)')
                    continue
                df_1.groupby(col).size().plot.bar(rot=0,color='g') #plotting Bar chart
                ax.set_title(str(col)+' (*categ. '+str(len(df_1[col].value_counts()))+' cat.)')
                if len(df_1[col].value_counts())>20:
                    ax.tick_params(bottom=False,labelbottom=False)
                    
        elif plt_type == 'box':
            msk = ~np.isnan(df_n.loc[:,col])
            ax.boxplot(df_n.loc[msk,col])  #plotting Box plot (used a mask to filter the null values)
            #ax.boxplot(df.loc[:,col])
            ax.set_title(col)
        elif plt_type == 'scat':
            ax.scatter(df_n.iloc[:,0],df_n.loc[:,col],s=0.1,alpha=0.1) #plotting scatterd plot against the index of the D.F.
            ax.set_title(col)
            
        elif plt_type == 'target_scat':
            ax.scatter(df_n.loc[:,col],df_n.loc[:,target],s=1,alpha=0.1,color='b') #plotting scatterd plot of num. variables against the 'target' column of the D.F.
            #ax.set_title(col)
            ax.set_xlabel(col)
            
        elif plt_type == 'target_hexbin':
            ax = df_n.plot.hexbin(ax=ax,x=col, y=target,gridsize=50, sharex=False) #plotting hexbin plot
            ax.set_title(col)
            
        elif (plt_type == 'target_histplot'):
            if (df_1[target].dtype not in numerics):
                ax.set_title(str(col)+' (*categ. '+str(len(df_1[col].value_counts()))+' cat.)')
                if (len(df_1[col].value_counts())<=700):
                    sns.countplot(ax=ax, data = df_1,x = col,hue = target,alpha=0.7) #plotting countplot of cat. variables against the 'target' column
                    if len(df_1[col].value_counts())>20:
                        ax.tick_params(bottom=False,labelbottom=False)
                elif (df_1[col].dtype in numerics):
                        sns.histplot(df_1, x=col, hue=target, element="poly",ax=ax) #plotting histplot of num. variables against the 'target' column
                        ax.set_title(str(col)+' (*num)')
#                         ax.plt.legend(fontsize='xx-small', title_fontsize='xx-small')
#                         plt.setp(ax.get_legend().get_texts(), fontsize='5') # for legend text
#                         plt.setp(ax.get_legend().get_title(), fontsize='32') # for legend title                        
            elif df_1[target].dtype in numerics:
                if (df_1[col].dtype not in numerics):
                    ax.set_title(str(col)+' (*categ. '+str(len(df_1[col].value_counts()))+' cat.)')
                    if (len(df_1[col].value_counts())<=5):
                        sns.histplot(df_1, x=target, hue=col, element="poly",ax=ax) #plotting histplot of num. variables against the 'target' column
                elif (df_1[col].dtype in numerics):
                    ax = df_1.plot.hexbin(ax=ax,x=target, y=col,gridsize=30, sharex=False, cmap="cubehelix_r") #plotting hexbin plot
#                     continue
                           
            
                
    plt.tight_layout()
    plt.show() 
   
    return 

plt_summary(df_Bitcoin_price_tweets,'hist','diff')
plt_summary(df_Bitcoin_price_tweets,'box','diff')
plt_summary(df_Bitcoin_price_tweets,"target_scat",'diff')

Plot Type:  hist
Number of columns:  5

Plot Type:  box
Number of Numeric columns:  5

Plot Type:  target_scat
Number of Numeric columns:  5

df_otl = df_Bitcoin_price_tweets.copy()

q1=df_otl.quantile(0.25)
q3=df_otl.quantile(0.75)
iqr=q3-q1
pmin=q1-iqr*1.5
pmax=q3+iqr*1.5
otl_cols = ['diff','count','polarity','subj','user_followers']
for n,col in enumerate(otl_cols):  
    df_otl.loc[:,col]=df_otl.loc[:,col].where(df_otl.loc[:,col].between(pmin[col],pmax[col]))
        
df_Bitcoin_price_tweets = df_otl.copy()

plt.figure(figsize=(20,10))
cmap1=sns.color_palette("Blues",2)
sns.heatmap(df_Bitcoin_price_tweets.isnull(),cmap=cmap1)
plt.tight_layout()
plt.show()

df_Bitcoin_price_tweets.dropna(inplace=True)
df_Bitcoin_price_tweets.reset_index(drop=True,inplace= True)
print('df_Bitcoin_price_tweets dimensions= ',df_Bitcoin_price_tweets.shape)

df_Bitcoin_price_tweets dimensions=  (29437, 5)

plt_summary(df_Bitcoin_price_tweets,'hist','diff')
plt_summary(df_Bitcoin_price_tweets,'box','diff')
plt_summary(df_Bitcoin_price_tweets,"target_scat",'diff')

Plot Type:  hist
Number of columns:  5

Plot Type:  box
Number of Numeric columns:  5

Plot Type:  target_scat
Number of Numeric columns:  5

X=df_Bitcoin_price_tweets.copy()

target='diff'
inputb='polarity'

# standardization 
# scaler= StandardScaler() 
# X_scaled = scaler.fit_transform(X)
# X=pd.DataFrame(X_scaled,columns=X.columns)

y = X[target].copy() # assigning the target to vector y
X = X[inputb]

X = sm.add_constant(X) #adding constant that will represent the intercept of the MLR model

# Splitting the data into train and test portions
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.30)

# LR implementation
linear_regression = sm.OLS(y_train,X_train)
fitted_model = linear_regression.fit()
residuals =fitted_model.predict(X_test)-y_test
itercept= fitted_model.params[0]
coefficient =fitted_model.params[1]

# MLR summary:
print(fitted_model.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                 -0.000
Method:                 Least Squares   F-statistic:                    0.8079
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.369
Time:                        23:32:24   Log-Likelihood:            -1.0975e+05
No. Observations:               20605   AIC:                         2.195e+05
Df Residuals:                   20603   BIC:                         2.195e+05
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.4353      0.433     -1.006      0.315      -1.284       0.413
polarity       0.2600      0.289      0.899      0.369      -0.307       0.827
==============================================================================
Omnibus:                        3.557   Durbin-Watson:                   1.991
Prob(Omnibus):                  0.169   Jarque-Bera (JB):                3.580
Skew:                          -0.021   Prob(JB):                        0.167
Kurtosis:                       3.049   Cond. No.                         2.26
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

# Linear regression plot
fig, ax = plt.subplots(figsize=(8, 6))
#plt.yscale('log')
plt.scatter((X[inputb]),y,s=5,label='Data points')
line= range(int(X[inputb].min()),int(X[inputb].max()+1))
line2 = sm.add_constant(line)
plt.plot(line,fitted_model.predict(line2),'r--',label='Regression Line')
plt.legend(loc='upper right')
plt.ylabel(target)
plt.xlabel(inputb)
plt.grid()
plt.show()

#  MLR examination plots
residuals.hist(bins = 50)
plt.title('Residuals')
plt.xlabel('residual')
plt.show()

plt.scatter(X_test.index,residuals,s=10,alpha=0.4,c=y_test,cmap='plasma')
plt.title('Residuals positions (colored by "'+ target +'")');plt.xlabel('Test data index');plt.ylabel('Test data residuals')
plt.colorbar();plt.grid('on')
plt.show()

#  QQ-Plot
fig, ax = plt.subplots(figsize=(7, 4))
stats.probplot(residuals, plot=ax) # QQ-Plot
plt.title('Residuals normality')
plt.tight_layout()
plt.show()

def linear_reg_test(df,target,inputb):
    X=df.copy()

    # standardization 
#     scaler= StandardScaler() 
#     X_scaled = scaler.fit_transform(X)
#     X=pd.DataFrame(X_scaled,columns=X.columns)

    y = X[target].copy() # assigning the target to vector y
    X = X[inputb]

    X = sm.add_constant(X) #adding constant that will represent the intercept of the MLR model

    # Splitting the data into train and test portions
    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.10)

    # LR implementation
    linear_regression = sm.OLS(y_train,X_train)
    fitted_model = linear_regression.fit()
    residuals =fitted_model.predict(X_test)-y_test

    # MLR summary:
    print(fitted_model.summary())
    r2=fitted_model.rsquared; 
    p_min=min(fitted_model.pvalues[1:])
    p_values=str(fitted_model.pvalues[1:]).replace('\n',', ')
    coefs=str(fitted_model.params[1:]).replace('\n',', ')
    
    
#     MLR examination plots
    residuals.hist(bins = 50)
    plt.title('Residuals')
    plt.xlabel('residual')
    plt.show()

    plt.scatter(X_test.index,residuals,s=20,alpha=0.4,c=y_test,cmap='plasma')
    plt.title('Residuals positions (colored by "'+ target +'")');plt.xlabel('Test data idx');plt.ylabel('Test data residuals')
    plt.colorbar();plt.grid('on')
    plt.show()

#     QQ-Plot
    fig, ax = plt.subplots(figsize=(4, 4))
    stats.probplot(residuals, plot=ax) # QQ-Plot
    plt.title('Residuals normality')
    plt.tight_layout()
    plt.show()
    
    return r2, p_min,p_values[:-14],coefs[:-14]

def all_targets_all_inputs(df,targets,inputs,frame):
    test_number=0
    test_ser=[];r2=[];p_min=[];p_values=[];target_list=[];input_list=[];number_of_inputs=[];frame_list=[];coefs_list=[]
    for targeta in targets:        
        inputs_list=[]
        # one input
        for input1 in inputs:
            inputb = input1
            test_number=test_number+1
            print('Time_Frame= ',frame,'Min')
            print('Test number= ',test_number)
            print('target= ',targeta)
            print('number of inputs= 1')
            print('inputs= ',inputb)
            #print(50*'-')
            inputs_list.append(inputb)
            r2_val, p_min_val,p_values_val,coefs_val=linear_reg_test(df,targeta,inputb)
            print(100*'-')
            coefs_list.append(coefs_val);frame_list.append(frame);test_ser.append(test_number);r2.append(r2_val);p_min.append(p_min_val);p_values.append(p_values_val);target_list.append(targeta);input_list.append(inputb);number_of_inputs.append(1)
            
            
        # two inputs
        for input1 in inputs:
            for input2 in inputs:
                inputb=[]
                if ((input1 == input2)):
                    continue
                inputb.append(input1)
                inputb.append(input2)
                if ((set(inputb) in inputs_list)):
                    continue
                inputs_list.append(set(inputb))
                #print(iputs_list)
                test_number=test_number+1
                print('Time_Frame= ',frame,'Min')
                print('Test number= ',test_number)
                print('target= ',targeta)
                print('number of inputs= ',len(inputb))
                print('inputs= ',inputb)
                #print(50*'-')
                r2_val, p_min_val,p_values_val,coefs_val=linear_reg_test(df,targeta,inputb)
                print(100*'-')
                coefs_list.append(coefs_val);frame_list.append(frame);test_ser.append(test_number);r2.append(r2_val);p_min.append(p_min_val);p_values.append(p_values_val);target_list.append(targeta);input_list.append(inputb);number_of_inputs.append(len(inputb))
                
        # three inputs
        for input1 in inputs:
            for input2 in inputs:
                for input3 in inputs:
                    inputb=[]
                    if ((input1 == input2) or (input1 == input3) or (input2 == input3)):
                        continue
                    inputb.append(input1)
                    inputb.append(input2)
                    inputb.append(input3)
                    if ((set(inputb) in inputs_list)):
                        continue
                    inputs_list.append(set(inputb))
                    #print(iputs_list)
                    test_number=test_number+1
                    print('Time_Frame= ',frame,'Min')
                    print('Test number= ',test_number)
                    print('target= ',targeta)
                    print('number of inputs= ',len(inputb))
                    print('inputs= ',inputb)
                    #print(50*'-')
                    r2_val, p_min_val,p_values_val,coefs_val=linear_reg_test(df,targeta,inputb)
                    print(100*'-')
                    coefs_list.append(coefs_val);frame_list.append(frame);test_ser.append(test_number);r2.append(r2_val);p_min.append(p_min_val);p_values.append(p_values_val);target_list.append(targeta);input_list.append(inputb);number_of_inputs.append(len(inputb))
        # four inputs
        for input1 in inputs:
            for input2 in inputs:
                for input3 in inputs:
                    for input4 in inputs:
                        inputb=[]
                        if ((input1 == input2) or (input1 == input3) or (input1 == input4) or (input2 == input3) or (input2 == input4) or (input3 == input4)):
                            continue
                        inputb.append(input1)
                        inputb.append(input2)
                        inputb.append(input3)
                        inputb.append(input4)
                        if ((set(inputb) in inputs_list)):
                            continue
                        inputs_list.append(set(inputb))
                        #print(iputs_list)
                        test_number=test_number+1
                        print('Time_Frame= ',frame,'Min')
                        print('Test number= ',test_number)
                        print('target= ',targeta)
                        print('number of inputs= ',len(inputb))
                        print('inputs= ',inputb)
                        #print(50*'-')
                        r2_val, p_min_val,p_values_val,coefs_val=linear_reg_test(df,targeta,inputb)
                        print(100*'-')
                        coefs_list.append(coefs_val);frame_list.append(frame);test_ser.append(test_number);r2.append(r2_val);p_min.append(p_min_val);p_values.append(p_values_val);target_list.append(targeta);input_list.append(inputb);number_of_inputs.append(len(inputb))
                        
    data={'Test_no. for target':test_ser,'Target':target_list,'R2':r2,'No. of inputs':number_of_inputs,'min-p':p_min,'Inputs':input_list,'p-values':p_values,'Time_Frame':frame_list,'coefs_list':coefs_list}
    df_all_results = pd.DataFrame(data=data,columns = ['Test_no. for target','Target','R2','No. of inputs','min-p','Inputs','coefs_list','p-values','Time_Frame'])
    
    df_all_results.set_index('Test_no. for target',drop=True,inplace=True)
    return df_all_results        

targets= ['diff']
inputs=['count','user_followers','subj','polarity']

df_all_results=all_targets_all_inputs(df_Bitcoin_price_tweets,targets,inputs,'1')

Time_Frame=  1 Min
Test number=  1
target=  diff
number of inputs= 1
inputs=  count
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                 -0.000
Method:                 Least Squares   F-statistic:                    0.1198
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.729
Time:                        23:32:27   Log-Likelihood:            -1.4117e+05
No. Observations:               26493   AIC:                         2.823e+05
Df Residuals:                   26491   BIC:                         2.824e+05
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.3084      0.629     -0.491      0.624      -1.541       0.924
count          0.0704      0.204      0.346      0.729      -0.329       0.469
==============================================================================
Omnibus:                        4.320   Durbin-Watson:                   1.999
Prob(Omnibus):                  0.115   Jarque-Bera (JB):                4.442
Skew:                          -0.012   Prob(JB):                        0.108
Kurtosis:                       3.059   Cond. No.                         6.86
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  2
target=  diff
number of inputs= 1
inputs=  user_followers
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     1.075
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.300
Time:                        23:32:29   Log-Likelihood:            -1.4114e+05
No. Observations:               26493   AIC:                         2.823e+05
Df Residuals:                   26491   BIC:                         2.823e+05
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const              0.2368      0.392      0.604      0.546      -0.531       1.005
user_followers -5.945e-05   5.73e-05     -1.037      0.300      -0.000    5.29e-05
==============================================================================
Omnibus:                        5.023   Durbin-Watson:                   1.996
Prob(Omnibus):                  0.081   Jarque-Bera (JB):                5.097
Skew:                          -0.021   Prob(JB):                       0.0782
Kurtosis:                       3.053   Cond. No.                     8.75e+03
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 8.75e+03. This might indicate that there are
strong multicollinearity or other numerical problems.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  3
target=  diff
number of inputs= 1
inputs=  subj
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     2.801
Date:                Sun, 25 Jul 2021   Prob (F-statistic):             0.0942
Time:                        23:32:31   Log-Likelihood:            -1.4116e+05
No. Observations:               26493   AIC:                         2.823e+05
Df Residuals:                   26491   BIC:                         2.823e+05
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.4893      0.489     -1.001      0.317      -1.448       0.469
subj           0.7865      0.470      1.674      0.094      -0.135       1.708
==============================================================================
Omnibus:                        5.692   Durbin-Watson:                   1.996
Prob(Omnibus):                  0.058   Jarque-Bera (JB):                5.813
Skew:                          -0.021   Prob(JB):                       0.0547
Kurtosis:                       3.059   Cond. No.                         2.84
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  4
target=  diff
number of inputs= 1
inputs=  polarity
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     2.396
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.122
Time:                        23:32:32   Log-Likelihood:            -1.4119e+05
No. Observations:               26493   AIC:                         2.824e+05
Df Residuals:                   26491   BIC:                         2.824e+05
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.1801      0.383     -0.471      0.638      -0.930       0.570
polarity       0.3967      0.256      1.548      0.122      -0.106       0.899
==============================================================================
Omnibus:                        3.234   Durbin-Watson:                   1.994
Prob(Omnibus):                  0.198   Jarque-Bera (JB):                3.245
Skew:                          -0.018   Prob(JB):                        0.197
Kurtosis:                       3.041   Cond. No.                         2.25
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  5
target=  diff
number of inputs=  2
inputs=  ['count', 'user_followers']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                 -0.000
Method:                 Least Squares   F-statistic:                    0.3323
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.717
Time:                        23:32:34   Log-Likelihood:            -1.4114e+05
No. Observations:               26493   AIC:                         2.823e+05
Df Residuals:                   26490   BIC:                         2.823e+05
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const              0.0106      0.629      0.017      0.987      -1.222       1.244
count              0.0794      0.223      0.357      0.721      -0.357       0.516
user_followers -5.095e-05   6.26e-05     -0.815      0.415      -0.000    7.17e-05
==============================================================================
Omnibus:                        4.468   Durbin-Watson:                   1.983
Prob(Omnibus):                  0.107   Jarque-Bera (JB):                4.592
Skew:                          -0.013   Prob(JB):                        0.101
Kurtosis:                       3.059   Cond. No.                     1.46e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.46e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  6
target=  diff
number of inputs=  2
inputs=  ['count', 'subj']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                 -0.000
Method:                 Least Squares   F-statistic:                    0.4587
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.632
Time:                        23:32:35   Log-Likelihood:            -1.4123e+05
No. Observations:               26493   AIC:                         2.825e+05
Df Residuals:                   26490   BIC:                         2.825e+05
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.1964      0.632     -0.311      0.756      -1.436       1.043
count         -0.1117      0.263     -0.425      0.671      -0.627       0.403
subj           0.5657      0.605      0.935      0.350      -0.620       1.752
==============================================================================
Omnibus:                        3.423   Durbin-Watson:                   2.017
Prob(Omnibus):                  0.181   Jarque-Bera (JB):                3.454
Skew:                          -0.016   Prob(JB):                        0.178
Kurtosis:                       3.046   Cond. No.                         7.13
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  7
target=  diff
number of inputs=  2
inputs=  ['count', 'polarity']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     2.022
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.132
Time:                        23:32:37   Log-Likelihood:            -1.4122e+05
No. Observations:               26493   AIC:                         2.825e+05
Df Residuals:                   26490   BIC:                         2.825e+05
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.0820      0.631     -0.130      0.897      -1.318       1.154
count         -0.1014      0.222     -0.458      0.647      -0.536       0.333
polarity       0.5515      0.278      1.983      0.047       0.006       1.097
==============================================================================
Omnibus:                        3.489   Durbin-Watson:                   1.987
Prob(Omnibus):                  0.175   Jarque-Bera (JB):                3.525
Skew:                          -0.016   Prob(JB):                        0.172
Kurtosis:                       3.047   Cond. No.                         7.22
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  8
target=  diff
number of inputs=  2
inputs=  ['user_followers', 'subj']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     1.596
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.203
Time:                        23:32:39   Log-Likelihood:            -1.4120e+05
No. Observations:               26493   AIC:                         2.824e+05
Df Residuals:                   26490   BIC:                         2.824e+05
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const              0.0870      0.519      0.168      0.867      -0.931       1.105
user_followers -8.715e-05   5.83e-05     -1.496      0.135      -0.000     2.7e-05
subj               0.6011      0.479      1.256      0.209      -0.337       1.539
==============================================================================
Omnibus:                        3.510   Durbin-Watson:                   2.018
Prob(Omnibus):                  0.173   Jarque-Bera (JB):                3.538
Skew:                          -0.017   Prob(JB):                        0.171
Kurtosis:                       3.045   Cond. No.                     1.44e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.44e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  9
target=  diff
number of inputs=  2
inputs=  ['user_followers', 'polarity']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     2.019
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.133
Time:                        23:32:41   Log-Likelihood:            -1.4111e+05
No. Observations:               26493   AIC:                         2.822e+05
Df Residuals:                   26490   BIC:                         2.822e+05
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const             -0.0687      0.442     -0.155      0.876      -0.935       0.797
user_followers -8.178e-05   5.76e-05     -1.419      0.156      -0.000    3.11e-05
polarity           0.3968      0.257      1.544      0.123      -0.107       0.900
==============================================================================
Omnibus:                        6.137   Durbin-Watson:                   1.985
Prob(Omnibus):                  0.046   Jarque-Bera (JB):                6.326
Skew:                          -0.019   Prob(JB):                       0.0423
Kurtosis:                       3.065   Cond. No.                     1.03e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.03e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  10
target=  diff
number of inputs=  2
inputs=  ['subj', 'polarity']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     1.605
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.201
Time:                        23:32:42   Log-Likelihood:            -1.4117e+05
No. Observations:               26493   AIC:                         2.823e+05
Df Residuals:                   26490   BIC:                         2.824e+05
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.5265      0.489     -1.076      0.282      -1.486       0.433
subj           0.5273      0.561      0.940      0.347      -0.572       1.627
polarity       0.2331      0.306      0.762      0.446      -0.366       0.832
==============================================================================
Omnibus:                        5.971   Durbin-Watson:                   2.010
Prob(Omnibus):                  0.051   Jarque-Bera (JB):                6.054
Skew:                          -0.025   Prob(JB):                       0.0485
Kurtosis:                       3.054   Cond. No.                         4.17
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  11
target=  diff
number of inputs=  3
inputs=  ['count', 'user_followers', 'subj']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     1.819
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.141
Time:                        23:32:44   Log-Likelihood:            -1.4120e+05
No. Observations:               26493   AIC:                         2.824e+05
Df Residuals:                   26489   BIC:                         2.824e+05
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const             -0.0418      0.631     -0.066      0.947      -1.280       1.196
count             -0.1770      0.281     -0.629      0.529      -0.729       0.375
user_followers -6.774e-05   6.28e-05     -1.079      0.281      -0.000    5.53e-05
subj               1.1914      0.605      1.968      0.049       0.005       2.378
==============================================================================
Omnibus:                        3.472   Durbin-Watson:                   2.004
Prob(Omnibus):                  0.176   Jarque-Bera (JB):                3.504
Skew:                          -0.016   Prob(JB):                        0.173
Kurtosis:                       3.046   Cond. No.                     1.46e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.46e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  12
target=  diff
number of inputs=  3
inputs=  ['count', 'user_followers', 'polarity']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                 -0.000
Method:                 Least Squares   F-statistic:                    0.8833
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.449
Time:                        23:32:46   Log-Likelihood:            -1.4120e+05
No. Observations:               26493   AIC:                         2.824e+05
Df Residuals:                   26489   BIC:                         2.824e+05
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const             -0.2260      0.630     -0.358      0.720      -1.462       1.010
count              0.0437      0.241      0.181      0.856      -0.429       0.516
user_followers -6.433e-05   6.27e-05     -1.026      0.305      -0.000    5.86e-05
polarity           0.3229      0.278      1.160      0.246      -0.223       0.869
==============================================================================
Omnibus:                        3.969   Durbin-Watson:                   2.013
Prob(Omnibus):                  0.137   Jarque-Bera (JB):                3.993
Skew:                          -0.020   Prob(JB):                        0.136
Kurtosis:                       3.044   Cond. No.                     1.46e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.46e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  13
target=  diff
number of inputs=  3
inputs=  ['count', 'subj', 'polarity']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     1.306
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.271
Time:                        23:32:48   Log-Likelihood:            -1.4115e+05
No. Observations:               26493   AIC:                         2.823e+05
Df Residuals:                   26489   BIC:                         2.823e+05
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.0092      0.631     -0.015      0.988      -1.245       1.227
count         -0.3434      0.262     -1.309      0.191      -0.858       0.171
subj           0.9995      0.667      1.499      0.134      -0.308       2.307
polarity       0.1587      0.307      0.518      0.605      -0.442       0.760
==============================================================================
Omnibus:                        3.621   Durbin-Watson:                   1.998
Prob(Omnibus):                  0.164   Jarque-Bera (JB):                3.662
Skew:                          -0.016   Prob(JB):                        0.160
Kurtosis:                       3.047   Cond. No.                         8.00
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  14
target=  diff
number of inputs=  3
inputs=  ['user_followers', 'subj', 'polarity']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     1.269
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.283
Time:                        23:32:49   Log-Likelihood:            -1.4120e+05
No. Observations:               26493   AIC:                         2.824e+05
Df Residuals:                   26489   BIC:                         2.824e+05
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const             -0.1396      0.521     -0.268      0.789      -1.160       0.881
user_followers -7.539e-05   5.85e-05     -1.289      0.197      -0.000    3.93e-05
subj               0.4113      0.570      0.721      0.471      -0.707       1.529
polarity           0.2592      0.306      0.848      0.397      -0.340       0.858
==============================================================================
Omnibus:                        4.413   Durbin-Watson:                   1.989
Prob(Omnibus):                  0.110   Jarque-Bera (JB):                4.473
Skew:                          -0.019   Prob(JB):                        0.107
Kurtosis:                       3.051   Cond. No.                     1.53e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.53e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

----------------------------------------------------------------------------------------------------
Time_Frame=  1 Min
Test number=  15
target=  diff
number of inputs=  4
inputs=  ['count', 'user_followers', 'subj', 'polarity']
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                   diff   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                  0.000
Method:                 Least Squares   F-statistic:                     1.287
Date:                Sun, 25 Jul 2021   Prob (F-statistic):              0.273
Time:                        23:32:51   Log-Likelihood:            -1.4122e+05
No. Observations:               26493   AIC:                         2.824e+05
Df Residuals:                   26488   BIC:                         2.825e+05
Df Model:                           4                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
const             -0.5065      0.633     -0.800      0.424      -1.748       0.735
count              0.0640      0.283      0.227      0.821      -0.490       0.618
user_followers -8.781e-05   6.29e-05     -1.397      0.162      -0.000    3.54e-05
subj               0.7073      0.669      1.057      0.291      -0.604       2.019
polarity           0.1359      0.308      0.442      0.659      -0.467       0.739
==============================================================================
Omnibus:                        3.082   Durbin-Watson:                   2.001
Prob(Omnibus):                  0.214   Jarque-Bera (JB):                3.101
Skew:                          -0.015   Prob(JB):                        0.212
Kurtosis:                       3.043   Cond. No.                     1.56e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.56e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

----------------------------------------------------------------------------------------------------

df_all_results.drop(['min-p','Inputs','Time_Frame'],axis=1).sort_values(by='No. of inputs',axis=0,ascending=True)

	Target	R2	No. of inputs	coefs_list	p-values
Test_no. for target
1	diff	0.000005	1	count 0.070443,	count 0.729298,
2	diff	0.000041	1	user_followers -0.000059,	user_followers 0.299806,
3	diff	0.000106	1	subj 0.786481,	subj 0.094235,
4	diff	0.000090	1	polarity 0.396712,	polarity 0.121673,
5	diff	0.000025	2	count 0.079408, user_followers -0.000051,	count 0.721248, user_followers 0.415360,
6	diff	0.000035	2	count -0.111659, subj 0.565706,	count 0.670908, subj 0.349875,
7	diff	0.000153	2	count -0.101364, polarity 0.551455,	count 0.647312, polarity 0.047400,
8	diff	0.000121	2	user_followers -0.000087, subj 0.601098,	user_followers 0.134726, subj 0.209214,
9	diff	0.000152	2	user_followers -0.000082, polarity 0.396817,	user_followers 0.155792, polarity 0.122542,
10	diff	0.000121	2	subj 0.527257, polarity 0.233137,	subj 0.347208, polarity 0.445805,
11	diff	0.000206	3	count -0.177011, user_followers -0.000068, subj 1.191447,	count 0.529387, user_followers 0.280594, subj 0.049024,
12	diff	0.000100	3	count 0.043654, user_followers -0.000064, polarity 0.322860,	count 0.856198, user_followers 0.305005, polarity 0.246176,
13	diff	0.000148	3	count -0.343421, subj 0.999482, polarity 0.158694,	count 0.190552, subj 0.133992, polarity 0.604717,
14	diff	0.000144	3	user_followers -0.000075, subj 0.411313, polarity 0.259154,	user_followers 0.197436, subj 0.470869, polarity 0.396600,
15	diff	0.000194	4	count 0.064050, user_followers -0.000088, subj 0.707264, polarity 0.135881,	count 0.820749, user_followers 0.162372, subj 0.290501, polarity 0.658607,